We adopt the transition-based method for dependency parsing by nivre2003efficient.
The action set of arc-standard system [1]} is employed.
We build an SVM classifier to predict most possible transition action for given configuration.
We adopt the N-gram features, positional features, length features and dependency features for top-2 EDUs in the stack and top EDU in the buffer, which can be referred from [2]}, [3]}
